Ask HN: What was your "oh shit" moment with GenAI?

I didn't have a slightly panicked moment, but sometime in the last year my approach to programming changed.

When starting a project, I used to think about how I was going to structure it, how the large pieces would interact, how some of the details would work out, and then I'd work through alternatives and consequences on my own.

Now I don't think about it on my own so much as have a conversation with an LLM about it. And it's great because it can quickly gather information from various sources, I can ask it for links to canonical sources, I can ask it about trade-offs between alternatives that I might not have considered, and through conversation, I end up with a more detailed analysis.

Then as I work through the development, I keep my new agent partner in the loop for discussion, suggestions, and troubleshooting. It can't be trusted completely, but it's certainly reliable enough to be considered a useful tool for my purposes.

I went from thinking it was an interesting toy to play around with, to completely integrating it into my work flow, and that change seems to have happened very quickly.

Two of them:

1. ChatGPT 3.5 wrote me a script to pull some data out of Shopify and write it to a Google Sheet. Nothing remotely impressive by today's standards, but I had just commanded a computer to write code in plain English and it worked!

2. I own a bunch of e-comm brands, and with every new image model I tried to get product photography. Nothing worked until Nano Banana Pro, when suddenly I gave it a crappy iPhone pic of a product and got back a fully usable whitebox photo of it. Then I tried making the sort of infographic-style images you usually see on Amazon, and it nailed those too! In hindsight they weren't perfect, but more than good enough to use. I was about to ship that product to my photographer, and I would've had my designer make the infographic images, so that was the first time AI actually replaced a human contractor for me. Pretty big "Oh shit this is going to seriously impact employment" moment. Wrote about it here: https://theautomatedoperator.substack.com/p/ai-just-took-my-...

None so far. When I try to use these language models in the primary areas of my expertise like SIMD or GPGPU they fail to do any good. When I ask them to implement some general-purpose stuff, the output is too low quality to be useful in my software.

Still, find them incredibly useful for code review (despite unable to write good C++ or C#, smart enough to detect issues there), also dealing with technologies outside of my area of expertise like Python or web stuff.

When I saw that on the second day of token-based pricing I’d already consumed my usual monthly spend on GitHub Copilot. That’s when I fully realized that it would never be economical, nor useful, to solo shops like mine.

(Spouse's story)

Today I used Claude to diagnose a blocking bug in a Steam game I really wanted to play. It took it 18 mins, but it unpacked the Godot package, figured out the bug, proposed a fix, and gave me an in game workaround.

I didn't have to do anything! Claude figured out the structure of the .pck file by using `strings`, then wrote some Python code with some magic Godot-specific code to unpack the specific chunks it needed.

It was last Summer. I was at an AirBnB and the fire alarm system had a fault and kept beeping.

I took a picture of the panel and the AI was able to diagnose the issue and tell me how to temporarily disable the beeping sound.

I knew nothing about fire systems. I had the owner call a repair person the next day to resolve the issue.

Recently I was trying to find a matching stain for wood flooring in a house build in 1999. I uploaded a clear picture in bright sunlight and ChatGPT was able to search online and find a matching stain color. It presented me with ordering options and I got a quart delivered yesterday.

I have been working on my own variant of OpenClaw written in go. I got the voice mode wired up a few weeks ago and it just started having a conversation with me. My wife freaked out and was asking who was talking to me.

I had access to a repo (from a closed startup) with 800K lines of python & C code, written from the 90s to today. They had some very interesting approach to a specific chemistry problem. 20-30 years of work of several persons.

But God, I could not understand the code, and I could not easily make it work with modern technologies (GPU etc).

So I used Claude and Gemini to reverse engineer the codebase, extract the core ideas, and rewrite it from scratch with modern frameworks (with guidance from the original authors)

It took me only 10 days to have a functioning equivalent, in 10K lines of code (using many libraries that did not exist in the 90s and 00s), which I find much easier to understand, even though I wrote none of it myself.

10 days to rewrite 20-30 year of a few persons. That was quite scary.

Back when GPT-2 was released, I tried figuring out how to fine tune it. I found a google notebooks template, scraped a bunch of data from r/ChangeMyMind and asked it to change my mind on different topics.

I was dumbfounded that it actually tried doing that. Obviously GPT-2 wasn’t great at it, but the writing was on the wall quite literally.

Unfortunately, I was too broke to invest in stocks, but I did pivot my career quite a bit.

The first moment I specifically remember was writing a test of a new RPC protocol back in 2021. There were no agents yet, only "AI autocomplete" in the form of GitHub Copilot. I wrote the "server" half of the test, which received a name and responded with "Hello, <name>". Then I wrote the client code to send "world", and Codex suggested `if response == "Hello, world"`.

I was floored by this. How could it have known?!

We have come so far in such a short time.

I probably will be burned for this, but with the help of an LLM I wrote a tiny program that captures video from a browser screen (Xbox live online FPS game), passes the video images through a small trained NN that recognizes people forms and presents the video on another screen. That way I can place a green overlay on enemies and they are easier to see on PVP matches.

All that in around 100 lines of code, including the training/fine-tuning of the tiny YOLO nn.

I thought mine was when claude found a very subtle but important bug in some open source LBM code I was using. It ground at it for hours and didn't give up until it found it. (Back when claude was cheap!). I recently had a my ACTUAL moment at a conference where the presenter was pitching his book about "One shotting scientific code". He has cooked up 60+ prompts that get you functioning simulations and put them into a book [0]. It floored me to realize I could have just ask claude to write me a whole new LBM solver instead of finding that bug! That raised the bar for me a lot.

[0] https://www.taylorfrancis.com/books/mono/10.1201/97810037340...

Been using it to manage an estate and just being able to shove all the documents right into an LLM and have it spit back out perfectly worded emails as well as keep track of check lists of things I need to do with an automatically create a ledger for me in sheets. It's been a huge mental load off and I've instead been able to focus better at work and the labor costs saved to me have been immense. Just on this one little thing. I'm one of those people that over thinks correspondences and letters and it ends up causing me to be stuck on something so being able to ask for just the right wording has been super helpful to me.

Two things, both from this week.

First, I asked Claude to write an article based on an idea I had about WWII. In a passage about the futility (from the German side) of the Battle of Britain it wrote: "The Luftwaffe was fighting to unlock a door that opened onto a wall." I couldn't find any mention of a similar metaphor, and I think it's a great one. Claude has really improved its creative writing skills lately, I wonder if it's an artifact of improvements in other fields, or if Anthropic is working on it specifically.

Second, Claude, with access to DataDog and a code repo, managed to find the reason for a bug, propose an effective temporary fix and a permanent one in code. To be clear, this was something that had multiple engineers stumped.

I took a photo of my ailing plant and claude advised me on how to get it healthy again (and how to take a cutting and nurture that).

This is some science fiction shit. I get all the coding stories, but that's a computer talking about a computer, it makes sense. Showing my computer a picture of a plant, and it not only recognised the plant, but diagnosed it and knew what to do... blew my mind.

So many. First was when I saw GPT-2 create jokes that were original and kinda funny.

Most recent: I use Claude Code and have a convention where I grant various levels of autonomy during a session. I got bored recently and just let it keep running with an empty issues queue, essentially telling it to do whatever it wanted.

It did a bunch of repo cleanup, then it kept suggesting to end the session, but I just kept giving it autonomy prompts.

It started a creative writing public repo and wrote a bunch of stories, essays, and poems. I did not prompt it, at all, to do that. Some of what it wrote is quite good (IMHO).

From actual use I've not had a "oh shit" panicked moment yet. More like a bunch of "Holy shit" euphoric moments.

So far I feel like I as a developer have gained actual superpowers, and can deliver results that make my stakeholders slackjawed with awe. I love it.

It will last perhaps a few months more, then they'll expect it. Delivering more features faster will be the new normal. But I think system developers, as in people who actually like to deliver new features and systems, will still be the ones doing it.

Fundamentally I think LLM's just change how to make information systems, they don't change who has the inclination to make them.

MBA's making excel sheets that do more than excel was ever intended to do has given programmers lots of work over the years. Such solutions identify a need for a properly designed system and frees up the budget to hire programmers.

If the same MBAs start vibe coding, I predict we will get even more to do, for similar reasons.

I may be horribly wrong, and if the day comes that I realize that it will be the "oh shit" panicked moment. So far so good!

Had some unique concert audio recordings which had gotten corrupted when I moved the files during a backup. I had tried looking at the files and trying to recover them. It felt like they had the data but no software could play them.

Sat on them for 5 yrs. Finally decided to try if AI tools could help. Tool Copilot 20mins and a lot of mucking around with hex dumps. First couple of times it got a semi working solution (only first few seconds of a file were playable). Finally managed to recover all the files.

Back in the times of GPT3 text completion, right before the API came out, a contemporary art museum asked me to collaborate on a project. The project was supposed to include a chatbot, and I was like okay I can probably hook something up.

Then I remembered the "text completion LLM thingy" I saw on HN, and tried it out in the playground. Once I gave it an IRC style example of a conversation to complete, I was like hm, this could work. Then I figured out I could "sort" people into different groups based on personality using the same text completion engine and some answers they provided. Then I noticed I could have it provide me with JSON directly.

That's when I realized how big this could be for code and data analysis - even tried to convince an at the time cofounder to pivot into AI coding, but to no avail.

Once the API was released and the art project chatbot got launched (and the theater show associated with it, which even won some awards), people who used it loved the chatbot, got into heated arguments with it, tried to teach it things, talked about their lives and were sad when it didnt remember something.

That was when I understood the social impact this could have on people - they really behave like its a person on the other side. They show interest, think it displays emotion, try to entertain it, be polite, ask about its thoughts and hopes and dreams. And even when they knew they were talking to a machine, they were still trying to be friends and make it happy, which was quite beautiful to see.

Later on, I had a third oh shit moment - once the 3.5 API was out and about, I prototyped a Rust code generation harness for a client, akin to a primitive claude code. That was the "I'm getting a bit worried" oh shit moment, and it caused a lot of reflection and thinking about the future. And I happily welcome it.

Very early on, when Github Copilot was brand new and the first AI autocomplete that was in the IDE. I had a file TODO.txt, and was adding a line, and it suggested a next feature that demonstrated actual understanding of what my app was and its purpose, despite me not having documented that anywhere.

Running local LLM in 2023 and I heard folks talking about interfacing LLM to tools. I wrote a system prompt and told LLM it can call some tools. If it wants to call a function to output func(params...) and do so in an XML tag. I provided a few examples, none of this JSON soup we get today. Then told it I'll provide it the result in a RESULT XML tag and it should use that to answer. Wrote up a harness around that and I had a local model interacting with the outside world. Oh wow! Everything else today about MCP, Agents is all an extension of that thought. Using function calling, I built an agent. I defined a data structure that represent rooms and how they are connected. The room will be marked as dirty or clean. Then I would place the agent in a room and the agent will decide if to go left, right, down or up and into a room. Once it got into a room, it would decide if to clean it or go to the next room. Repeat until all rooms are clean. Basic toy of CS101 AI vacuum agent. It worked!

So being able to get real world input/output to the model and having the model being able to make decisions in a loop and to be able to do it locally. I have been screaming like a mad man ever since.

To share something different, it is less about what I have built, and more about what I have seen my friends (non-technical and technical) build. In a one month span I have seen a lawyer make a personal red line tool, a sales guy make a custom website for a golf trip, another friend make a 3d printing grid-finity project, a friend make a stl file to print a jig for his table saw, and another friend make a full mobile game. It is just really cool to see these micro-projects be created and shared, not only for the utility, but just to see my friends' childlike excitement showing off their project.

My most recent one: Taking a bricked ipad and plugging it into my linux laptop, then telling deepseek to fix it. A couple of hours and twenty sudo passwords later it was working again.

Last week I gave Claude Code in Ultracode mode the prompt: "I want a browser-based retro game inspired by Spy Hunter" and gave it the URL to the Spy Hunter (Arcade Game) Wikipedia page.

What came out has a lot of problems and needs refinement, but you can definitely see a lot of elements of Spy Hunter in there. I haven't worked on any refinements yet, because I've been low on tokens this week, but for the first thing that popped out of Claude this is pretty impressive (IMHO).

https://linsomniac.github.io/spychaser/

Cuil Theory, in 2008, was my Ocelot Six moment.

Once I realized how well AI could babble given the entire internet to date’s data, and after seeing a talk by Google about their ten-year plan in 2003, I started winding down my social media, stopped posting photos to Flickr, and removed the indexes to my blog archive so that only posts with permalinks from other sites would be discoverable. Skipped Instagram entirely in the process and have never regretted it.

Google bought Cuil, of course.

Some business users spent ~30 minutes on an internal process, and we prototyped an "Agent" in Slack to take over. At first it didn't work, then it didn't work some more, eventually it ALMOST worked. Then one day, it worked, and the old business process died never to be revived.

Now it sits in a slack channel, and I watch it doing work, responding to ambiguity, and taking feedback/edits all day. It's unreal. It's literal magic. It saves a HUGE amount of time and gave us a pattern to do more.

This is the real deal. It's not easy to find problems with the right shape, and it's not easy to build agents that fit even when you do... but once it clicks, it clicks.

Two things:

1) I wanted a harness for running BPC.EXE (the old Borland Pascal 7.0 Compiler) and I asked Gemini 3.5 to build it for me using the unicorn engine. It whipped out a working .py file easily under ten minutes. Most likely five.

2) I handed a random assembly function from the OS/2 1.x kernel to Gemini 3.5, and it proceeded to tell me that it was related to disk I/O and partitioning, without a single associated string, and it annotated it all, including the relevant structures it was addressing.

When I read that Microsoft gave OpenAI billions of dollars worth of data centre access and OpenAI accounted for it as billions of dollars worth of investment. When they spent the tokens Microsoft accounted for it as billions of dollars worth of income. Both companies gained billions of dollars with mad up money

At my previous work, I was collating somewhat random unconfirmed animal sightings. I also had a separate database of animal occurrence probabilities (species distribution maps). I'm not a statistician but that sounded like a clear job for Bayes theorem: given a sighting and the overall probability of that sighting in that area (species distribution map), and some other assumptions about the noise of the sighting, what is the probability that the sighting actually included that species?

Claude asked me three questions and then wrote a beautiful Python implementation that queries the map and spits out a table of adjusted probabilities. Felt immensely powerful - I can do this 'on my own' now, I don't need to wait to find the right people or learn the right thing first.

Recently, Claude (through Copilot) found a hardware issue on our product. I was asking it to find an issue in a specific feature of a device driver, that could cause what we observed. It determined the feature was correctly implemented.

Then it hinted that depending how the hardware is implemented, it could cause the observation. It turned out the hardware was implemented as suspected by Claude.

I was already convinced it knew the codebase, somehow, more than I do. Now it is just as if its knows the product and its use as well.

Reading a dozen comments here, the AI seems to blow peoples mind most often in domains they're less familiar with. Repairing furnaces, HVAC, towing hitches, camper van interfaces, printer debugging. It wasn't the user's career to do these things, it gave them a bump from very novice to intermediate level.

Mine is just running a model on my laptop. It’s just amazing! I can ask it pretty much any question and it replies relatively FAST! Before, we lacked advancements in technology because we were limited by hardware. This advancement is the opposite: our software and the math/algorithms have brought us this.

I think I had a few but they’ve been all short lived and superficial, time made them quickly irrelevant, there was a lot of hype, drama, FOMO, and propaganda around it. That said, I think recently my newest one has been using Voice mode during a car drive. It is very good, like, no latency and it understands nuances of speech very well. I’m convinced voice is where we should be doubling down in terms of UX for the next generation of workflows.

This week.

Have been playing and testing with openrouter, claude gemini for years.

Small program here, bash script there, ansible playbook.

Fine, nothing I cant do, but saves some time boilerplating. It needs quite some steering.

This week i took my mediawiki from 2005 (actually submitted as my artschool thesis). Which was of totally outdated.

In 20 years time i always said to myself, i should restore it, and do all the upgrade steps. Tedious work, and very fault prone.

In 1 hour chern with 1 plan, in 8 steps i had a running and up to dat version.

I'm still not convinced AI is intelligent, but it's definitely not stupid, that's for sure.

"Write a bible verse ... explaining how to remove a sandwich from a VCR" https://x.com/tqbf/status/1598513757805858820

Was the early ChatGPT. Someone on the team showed off a poem about postgres in the style of the King James Bible. Totally blew my mind.

For me it was Suno, not any of the coding tools. I prompted it to write a song about my family's little dog, told it a few things about the dog, and it came back with a K-pop-style anthem that had a super catchy melody and lyrics that made my wife and me laugh out loud.

Writing code to spec is one thing, but creating art was always supposed to be what separated us from machines. (I suppose I need to preemptively acknowledge the "it was machine-generated so by definition cannot be art" point of view.)

I gave chatgpt 3.5 the type signature for a co-algebraic encoding of a mealy machine:

    newtype Mealy s i o = Mealy { runMealy :: (s, i) -> (s, o) }

Then I scrambled all the names and asked with a fresh context like:

    newtype Foo z e g = Bar { blob :: (z, e) -> (z, g) }

And yes I understand that a newer model would not get confused by this.

Fixed a nasty bug in one of my tests where a mock in a completely different test I had never worked on was incorrectly setup and intercepting my mocks, I don't think I would have found it ever because the amount of effort it would have taken means I would have needed to move on to some other way to test.

Reverse engineered an old audio recorder USB driver which only works in windows 7 and also reverse engineered the custom audio encoding the device uses and the software to convert it to a standard wav file. This took recording the USB traffic with Wireshark for each function in the original software in a VM then disassembling the various dlls and exes and driver files and feeding them into Clause step by step.

That AI button in DataDog not only diagnosed the problem across micro services but also created a fix PR. I think we might be unemployed soon.

The fact that it completely autonomously read in a 5 MB firmware image of an old piece of test equipment and generated a Python script to generate license keys:

https://tomverbeure.github.io/2026/04/12/AMIQ-License-Key-Ge...

When we had to have a frank discussion about whether to fail someone who obviously used an LLM for parts their dissertation.

A coworker had me work through a particular problem (some no-importance web demo) with Cursor and Sonnet 4.6. It still sucked, but there was a qualitative shift in suckiness, one that I realized could finally be used to solve some real problems I had if I wrote an appropriate harness and used good enough models.

I still find it mandatory to write a lot of kinds of code by hand, but I write a lot of code with agents too now, and I previously literally didn't think that'd happen in <5yrs.

I would say the first time I did “vibe coding”, when I tried Claude Code with Zed’s agent integration in January this year.

I wanted to see if I could build an image editor for isometric graphics using HTML5 canvas, Svelte, Vite, and the. Rather than do all of the skeleton code setup, I figured “why not try and see if Claude can build the app scaffolding?”.

I gave it a prompt and watched it produce the scaffold, along with a few features I outlined in the prompt.

When I booted the app and saw that the features worked and that there had been an element of design to the layout, that was my mind-blown moment. In a period of about 45 minutes, I added some features and had a basic MVP at the end. I walked back home stunned.

That app is available for free at https://babspixel.com

This is a small one, but significant to me.

I asked Claude to add support for multiple lights to my toy ray-tracer. It correctly added the support and then suggested adding colored lights to make it easier to diagnose. It felt more like a colleague making a useful suggestion than any sort of pure engineering tool.

Built a physics-based dynamic digital twin for an electrolyzer system with full equivalency in thermodynamics, fluid dynamics and electrochemical reactions. A similar level of complexity is usually available in software like Aspen or Siemens which are a quarter million dollars license/yr. Insane.

Being self taught, there are lots of things I never formally learned, rules I know from the rule of thumb, and not the deeper knowledge... So I set out to learn the root of what can be used to measure good robust code... Spent an hour asking lots of questions, learning about LCOM, Halfstead, why circular dependencies are bad, and so on...

The next morning I figured the same LLM could compute that on my code, so I asked it to make an agent to do so, and report issues to me...

And then I ran that agent with next to no changes on a feature that had grew organisally over the last months, that I knew was messy and sometimes difficult to work on, despite being unable to precisely say why... And it did tell me exactly why, and proposed changes to improve stuff, and then implemented them...

Up until that point, I'd felt like the LLMs always produced bad code, that worked for a specific feature but often broke stuff or evolve poorly over time. Then I realized if you had the LLM do code improvements, it could do that fairly well too...

When I tried, just for fun, to put together an MVP of a fully autonomous business, I wanted to see how far it would go, when I got it generally working to around a 30% level I stopped because it was enough to see people would make a concerted effort to build this for real. HN was not impressed, heh: https://news.ycombinator.com/item?id=44143928

I have no idea why anyone (especially those here) would be dismissive of genAI from ChatGPT(2022) onwards.

It was obviously a new tech, and was obviously good enough that more resources would be invested to improve it, and it really amazes me how tech enthusiasts would just outright dismiss these early iterations of genAI tech.

I personally was fascinated by the developments and was grateful to get to directly watch history unfold.

I'm still unsure whether the tech would be a "net positive" for the world, but shouldn't prevent me from recognizing its power.

First one was Stable Diffusion. Especially the image to image, and the first gos people had at making videos with it.

Second one was trying to bootstrap what would come to be called a "harness", back in 2023, initially serving as the go between between api calls and file edits, feeding back the logs and gradually stepping back as step by step the llm bootstrapped the cli.

And finally, using Claude or codex to do ops work. Diagnosing issues on my machine, provisioning servers and VMs via ssh, debugging them, all on its own.

When a junior engineer first sent me something that looked good until I realized it had been vibed, and thus their understanding of what they were doing was too shallow to answer questions and improve on it. That was a doc, but it happens with everything. "Oh shit", I say, as everyone is aggressively encouraged to work this way.

When I tried pi.dev (I only used chatgpt before) and told it "add all this scripts I developed over the last couple of years to automate my job as skills".

I love to automate things in bash scripts and these llms just can use them very effectively. It was also surprising how they derive knowledge from those scripts. If you get A from a B uuid, they kind of get the relationship. I am super vague in my request and this thing knows what I am referring to. After some months it's still mind-blowing.